US: Figure Eight, the Human-in-the-Loop artificial intelligence (AI) platform for data science and machine learning (ML) teams, announced today a new collaboration with Google Cloud making Figure Eight a data annotation launch partner for Google Cloud AutoML. AutoML is a suite of Machine Learning products that enables developers with limited machine learning expertise to train high-quality models by leveraging Google’s transfer learning and Neural Architecture Search technology.
With this partnership, aimed at helping AutoML customers with data collection, preparation, and model experimentation, Figure Eight will include AutoML specific templates and simplify the process of uploading training data to AutoML. Additionally Figure Eight will offer consultation to AutoML customers to assist with training data creation and provide guidance on AI fairness. This will provide AutoML customers with the ability to create high-quality training data to train, test and tune their machine learning models and continuously improve them.
“Democratizing AI so its benefits are available to companies of all shapes and sizes is a common mission for Google Cloud and Figure Eight,” said Robin Bordoli, CEO of Figure Eight. “We’re excited companies can use the Figure Eight platform to create the necessary training data to deploy computer vision machine learning models faster, more effectively and at a larger scale without having to build an internal team of machine learning experts.”
Figure Eight currently provides high-quality training data for Google teams such as Jigsaw. Extending Figure Eight’s platform capabilities for Google Cloud AutoML ensures Google customers will have the same ability to create customized high-quality training data needed to build quality models.
“Human labeling is a key need for our customers, and we are excited to partner with Figure Eight to enhance our support in this area,” said Francisco Uribe, Google Cloud AutoML Product Manager.
“We are excited to see this integration between Google Cloud and Figure Eight to accelerate our ability to train and build AI models for the real world,” said Etienne Manderscheid, VP of Engineering, AI – Machine Learning for Dialpad.